title: Enhancing classification accuracy of wrist movement by denoising sEMG signals.
abstract: This paper presents identification of 4 different wrist movements by analyzing fore-arm surface Electromyogram (sEMG) signals. In order to reduce noise picked up during the recording, wavelet based denoising is applied using Daubechies mother wavelet. Spectral features along with Wilson's amplitude were extracted and given to a linear classifier. The experimental result shows better recognition performance using the given features when denoising is applied. The maximum accuracy for identification of four wrist movement was 97.5% which is quite significant as compared to the previous researches.
authors.forename: Baqar
authors.initials: B
authors.lastname: Abbas
authors.forename: Omar
authors.initials: O
authors.lastname: Farooq
authors.forename: Yusuf
authors.initials: Y
authors.lastname: Uzzaman
authors.forename: Abid Ali
authors.initials: AA
authors.lastname: Khan
authors.forename: A L
authors.initials: AL
authors.lastname: Vyas
mesh_terms: D000465:Algorithms; D004576:Electromyography; D019277:Entropy; D006801:Humans; D009068:Movement; D010363:Pattern Recognition, Automated; D059629:Signal-To-Noise Ratio; D014953:Wrist / Q000502:physiology*